Most inference frameworks simply select computation methods based on some
heuristics [1]}, [2]}. Other frameworks such as Halide
[3]} and TVM [4]} optimize scheduling independently of employed algorithms.
Halide [3]} uses a genetic algorithm to search for an approximate optimal schedule solution.
In [6]}, improvemrnts to Halide are proposed by enabling larger
search spaces of schedules and employing a tree search algorithm based on beam
search. TVM [4]} separates hardware intrinsics from algorithms and
scheduling, supporting unseen devices. To optimize scheduling, they
predict running times of schedules with machine learning models based on XGBoost
[8]} by using features such as access to various memories.
